Relevant bibliographies by topics / Optimal irrigation schedule

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Optimal irrigation schedule'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Optimal irrigation schedule"

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Nixon, J. B., G. C. Dandy, and A. R. Simpson. "A genetic algorithm for optimizing off-farm irrigation scheduling." Journal of Hydroinformatics 3, no. 1 (2001): 11–22. http://dx.doi.org/10.2166/hydro.2001.0003.

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This paper examines the use of genetic algorithm (GA) optimization to identify water delivery schedules for an open-channel irrigation system. Significant objectives and important constraints are identified for this system, and suitable representations of these within the GA framework are developed. Objectives include maximizing the number of orders that are scheduled to be delivered at the requested time and minimizing variations in the channel flow rate. If, however, an order is to be shifted, the irrigator preference for this to be by ±24 h rather than ±12 h is accounted for. Constraints in
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Suryavanshi, A. R., and J. Mohan Reddy. "Optimal operation schedule of irrigation distribution systems." Agricultural Water Management 11, no. 1 (1986): 23–30. http://dx.doi.org/10.1016/0378-3774(86)90033-8.

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Fu, Qiang, Liyan Yang, Heng Li, et al. "Study on the Optimization of Dry Land Irrigation Schedule in the Downstream Songhua River Basin Based on the SWAT Model." Water 11, no. 6 (2019): 1147. http://dx.doi.org/10.3390/w11061147.

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The optimization of irrigation schedules is of great significance for saving water resources and ensuring food security. For the downstream of the Songhua River Basin, the key growth stages of crops were determined by the coupling degree between the effective precipitation and crop water requirement and the sensitivity indexes of the crop water production function. A Soil and Water Assessment Tool (SWAT) model was used to simulate 16 irrigation schedules in different scenarios. Taking four factors into consideration, Analytic Hierarchy Process (AHP)-Gray Interconnect Degree Analysis (GIDA) was
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Kurnia Hidayat, Asep, R. R. El Akbar, and A. S. Kosnayani. "Initial Dynamic System Design for Optimization of Gravity Irrigation Water Management (Open Gravitation Irrigation)." APTIKOM Journal on Computer Science and Information Technologies 4, no. 2 (2019): 74–80. http://dx.doi.org/10.11591/aptikom.j.csit.32.

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The available water condition is smaller than the need of irrigation water of agricultural land that is needed is an expression of conflict in the field. To overcome this is by the regulation of several parameters, including the regulation of planting schedule and reduction of land area. The model of irrigation system used is based on the optimum area regulation of cultivable land. Parameters used to overcome this problem are planting schedule, land failure risk and k factor. The k factor is the ratio of water availability to the water requirement. The output of this system is optimal net bene
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Soentoro, Edy Anto, Erlangga Perwira, Yadi Suryadi, and Winskayati. "Optimization of irrigation water use to increase the benefit of agricultural products." MATEC Web of Conferences 159 (2018): 01026. http://dx.doi.org/10.1051/matecconf/201815901026.

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Shortage of irrigation water supply in dry season prevents many farmers from growing their crops, and the annual benefit from agricultural products will decrease as much as the area of irrigation fields which have lack of water. The objective of this study is to determine the maximum benefit from agricultural products based on water availability, by determining the appropriate cropping pattern and maximum planting areas through linear programming. The case-study location is at Leuwi Kuya Irrigation Region. Planting schedule is selected based on minimum water shortage from simulation of 6 alter
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Li, Jiang, Xiyun Jiao, Hongzhe Jiang, Jian Song, and Lina Chen. "Optimization of Irrigation Scheduling for Maize in an Arid Oasis Based on Simulation–Optimization Model." Agronomy 10, no. 7 (2020): 935. http://dx.doi.org/10.3390/agronomy10070935.

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In arid regions, irrigation scheduling optimization is efficient in coping with the shortage of agricultural water resources. This paper developed a simulation–optimization model for irrigation scheduling optimization for the main crop in an arid oasis, aiming to maximize crop yield and minimize crop water consumption. The model integrated the soil water balance simulation model and the optimization model for crop irrigation scheduling. The simulation model was firstly calibrated and validated based on field experiment data for maize in 2012 and 2013, respectively. Then, considering the distri
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Zhai, Biying, Qiang Fu, Tianxiao Li, et al. "Rice Irrigation Schedule Optimization Based on the AquaCrop Model: Study of the Longtouqiao Irrigation District." Water 11, no. 9 (2019): 1799. http://dx.doi.org/10.3390/w11091799.

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As a crop with high water consumption, rice is an important measure of efforts to improve agricultural irrigation efficiency and alleviate the contradiction between the supply and demand of agricultural water resources. This paper takes the Longtouqiao irrigation district, in the hinterland of the Sanjiang Plain, a major rice-producing area in northern China, as an example, and the AquaCrop crop growth model and entropy-cloud model are jointly used to develop a rice irrigation schedule optimization model based on three kinds of typical rainfall years. Different irrigation schemes are establish
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Linker, Raphael, and Isaya Kisekka. "Model-Based Deficit Irrigation of Maize in Kansas." Transactions of the ASABE 60, no. 6 (2017): 2011–22. http://dx.doi.org/10.13031/trans.12341.

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Abstract. Maize is the dominant irrigated crop in Kansas. In recent years, as a result of declining groundwater levels in the Ogallala aquifer and diminished well capacities, farmers are turning to deficit irrigation strategies. This study demonstrates the potential of model-based optimization for determining adequate soil water depletion levels. CERES-Maize was used as surrogate crop, while the AquaCrop model was used in an optimization procedure that determined the optimal water depletion levels. A multi-objective optimization framework was used to determine several combinations of optimal w
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Wang, Dingbao, and Ximing Cai. "Optimal estimation of irrigation schedule – An example of quantifying human interferences to hydrologic processes." Advances in Water Resources 30, no. 8 (2007): 1844–57. http://dx.doi.org/10.1016/j.advwatres.2007.02.006.

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Robertson, D., M. Wood, and Q. J. Wang. "Estimating hydraulic parameters for a surface irrigation model from field conditions." Australian Journal of Experimental Agriculture 44, no. 2 (2004): 173. http://dx.doi.org/10.1071/ea02191.

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Border-check irrigation is the most common method of irrigating pastures in Northern Victoria. To make the best use of a border-check irrigation system, consideration needs to be given to the irrigation schedule and irrigation event management. Surface irrigation models can provide an inexpensive and rapid method for identifying optimal irrigation event performance. The most common difficulty encountered when using surface irrigation models is determining appropriate hydraulic parameters. Two experiments were conducted to investigate the relationship between hydraulic parameters of the Analyti
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Dissertations / Theses on the topic "Optimal irrigation schedule"

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Lategan, Eugene Lourens. "Determining of optimum irrigation schedules for drip irrigated Shiraz vineyards in the Breede River Valley." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/17866.

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Thesis (MScAgric)--Stellenbosch University, 2011.<br>ENGLISH ABSTRACT: Shiraz/110R grapevines, growing in a fine sandy loam soil in the Breede River Valley, were subjected to ten different drip irrigation strategies during the 2006/07, 2007/08 and 2008/09 seasons. Grapevines of the control treatment (T1) were irrigated at 30% to 40% plant available water (PAW) depletion throughout the growing season. Grapevines of three treatments were irrigated at 70% to 80% PAW depletion from bud break until véraison (i.e. when ca. 95% of grape berries have changed colour), followed by either irrigatio
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Al-Dhuhli, Hamed Sulaiman Ali. "Optimal irrigation scheduling under water quantity and quality constraints accounting for the stochastic character of regional weather patterns." Doctoral thesis, 2018. https://tud.qucosa.de/id/qucosa%3A33136.

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In arid countries both water scarcity and salinity represent the key factors which drastically limit crop yield in irrigated agriculture. In addition, relatively poor management practices with pretty low water productivity (WP) seriously aggravate the situation. In order to get “more crop per drop', i.e., to substantially improve water use efficiency, this thesis proposes the novel strategy NEMO (Nested Experimental, Modeling, and Optimization Strategy) for reliably evaluating an optimal irrigation schedule. The proposed methodology relies upon a close interaction between in-depth field invest
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Book chapters on the topic "Optimal irrigation schedule"

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Anaç, M. S., M. Ali Ul, I. H. Tuzel, D. Anaç, B. Okur, and H. Hakerlerler. "Optimum irrigation schedules for cotton under deficit irrigation conditions." In Crop Yield Response to Deficit Irrigation. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4752-1_15.

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Conference papers on the topic "Optimal irrigation schedule"

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"Using Variable Rate Irrigation to Determine Optimal Irrigation Schedule for Aerobic Rice Production." In 2015 ASABE / IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation - A Tribute to the Career of Terry Howell, Sr. Conference Proceedings. American Society of Agricultural and Biological Engineers, 2015. http://dx.doi.org/10.13031/irrig.20152143363.

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Xinghui, Qi, Qiu Lin, and Chen Xiaonan. "Application of Hyperplane Real Coding Genetic Algorithm in Optimal Irrigation Schedule of Crop." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.356.

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Journal articles
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